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Record W2164854053 · doi:10.1109/lawp.2015.2419174

A Vertical Reflection Ionospheric Clutter Model for HF Radar Used in Coastal Remote Sensing

2015· article· en· W2164854053 on OpenAlexafffund
Shuyan Chen, Weimin Huang, Eric W. Gill

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsIonosphereClutterIonospheric reflectionRadarReflection (computer programming)Radar horizonGeologyDoppler effectRemote sensingPhysicsIonospheric heaterContinuous-wave radarGeophysicsRadar imagingComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

A theoretical model of the high frequency (HF) radar ionospheric clutter for the case of vertical incidence is presented in this letter. Assuming a pulsed dipole source, the equation of the received electric field after vertical reflection by the ionosphere is first derived. Then the relationship between the ionospheric reflection coefficient and the ionospheric electron density is incorporated into the development of the ionospheric clutter power model. In order to investigate the power spectrum of this ionospheric clutter and its relative intensity to that of the first-order ocean clutter, the normalized ionospheric clutter power is simulated. Simulation results show that the horizontal ionospheric plasma drift velocity results in a Doppler spreading while the vertical motion introduces a simple Doppler shift.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.245
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2015
Admission routes2
Has abstractyes

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